This disclosure relates to reducing dimensionality of hyperspectral images based on an optimized set of basis vectors, and in particular, to reducing the dimensionality of hyperspectral images in multiple passes to further optimize the set of basis vectors in view of anomalous aspects of the hyperspectral images.
Black and white photographs of objects or geographic areas generally provide a two dimensional construct of the actual image or area captured in the photograph. In particular, each x-y coordinate in the two dimensional construct has a single value representing a blackness or whiteness of the particular coordinate in the captured image. As such, the human eye can perceive useful information about the objects or areas captured in black and white photographs based on differences between black, white, and/or various shades of gray. Although color photographs can add more visual information, for most purposes, the additional visual information represented in a color photograph is tied to the visual spectrum. In particular, each x-y coordinate in color photographs includes an approximation of the visual color spectrum for that particular coordinate spot, wherein the approximation is generally created by blending three color values, such as color values for red, green, and blue. As such, multispectral imaging systems (e.g., weather satellites, the Landsat Thematic Mapper remote imager, etc.) are often used in remote sensing and imaging applications, as multispectral imaging systems can capture light from frequencies beyond the visible light range (i.e., to extract additional information that the human eye may fail to perceive with receptors for red, green, and blue). For example, multispectral imaging systems may be capable of capturing infrared information in addition to information from within the visual spectrum. Even so, many multispectral imaging systems are nonetheless limited in their ability to perceive information that may otherwise be present in a different part of the visual spectrum.
Hyperspectral sensors, on the other hand, can collect image data across hundreds of spectral bands through a combination of technology associated with spectroscopy and remote imaging. As such, hyperspectral sensors can capture sufficient information to derive a contiguous spectrum for each pixel in an image. In particular, in addition to each pixel in the image having a gray or visible color value, each pixel in the image has a third dimension for a vector providing distinct information for the pixel over a large spectrum of wavelengths. Because different materials or objects tend to reflect wavelengths of visible and invisible light differently, the contiguous spectrum may be analyzed to separate and evaluate different wavelengths, thus permitting finer resolution and greater perception of information contained in the image. For example, inorganic materials such as minerals, chemical compositions, and crystalline structures tend to control the shape of the spectral curve and the presence and positions of specific absorption bands. In addition, the spectral reflectance curves of healthy green plants tend to have a characteristic shape based on various attributes of the plants (e.g., absorption effects from chlorophyll and other leaf pigments), while leaf structures may vary significantly depending on plant species or plant stress. Furthermore, if a combination of different materials are present at a given pixel in the image (e.g., biological, chemical, mineral, or other materials), the particular combination will provide a composite signal that can be analyzed and compared to known signal waveforms to derive information for the materials within the pixel.
As such, hyperspectral imaging systems have a significant ability to characterize targets, materials, and changes to an image, providing a detection granularity that exceeds the actual resolution of pixels in the image and a change identification capability that does not require pixel level registration. Hyperspectral imaging systems can therefore provide benefits in a wide array of practical applications, including image based surveillance for detecting chemical or biological weapons, assessing bomb damage to underground structures, identifying drug production and cultivation sites, and detecting friendly or enemy troops and vehicles beneath foliage or camouflage, among other things. However, because each pixel in a hyperspectral image carries information for a broad spectrum of wavelengths, hyperspectral images often include vast quantities of data that require significant cost, weight, size, and power investments to store, transmit, analyze, or otherwise process the images. For example, hyperspectral sensors are often located remotely on satellites or aircraft capable of imaging areas in excess of 500×500 kilometers per hour, which may result in the hyperspectral sensors generating anywhere from three to fifteen gigabits of data per second.
Consequently, although hyperspectral sensors can capture images with increased spatial content and resolution across greater wavelength spectrums, the large data volumes introduce latency problems, particularly in cases where the data needs to be processed in near real time. For example, if a hyperspectral image corresponds to a disaster area (e.g., coast lines ravaged by a tsunami, combat zones, or targets of a terrorist strike), transferring and processing hyperspectral image data in real time may be critical to preventing loss of life and property. In addition, various applications may benefit from processing the hyperspectral image data at a location where the image is collected and subsequently transmitting results of the processing or subsets of the image data to another location for further processing. However, given the volume of data corresponding to a hyperspectral image, processing the data locally may significantly tax local resources, potentially leading to further delays in transmitting the data to other locations. Although the transmission rate for hyperspectral images can be increased using existing lossy and/or lossless compression techniques, these techniques also suffer from various drawbacks.
For example, lossy data compression generally refers to a class of data compression algorithms where data may be compressed to produce a new data set that is different from the original data set, yet close enough to the original data set to be useful for certain applications (e.g., lossy compression formats may include the JPEG image format designed to provide images that respond well to the human eye, the MP3 audio format designed to remove data that is unlikely to be perceived by the human ear, etc.). As such, lossy compression methods are typically used to produce compressed data sets that nonetheless satisfy requirements for a particular application. However, in the context of an application that processes hyperspectral images, existing lossy compression methods typically remove information that is valuable and desired. For example, compressing a hyperspectral image into a JPEG image may remove information captured from spectral bands that the human eye cannot perceived, which may undermine the ability to characterize targets, materials, or changes to scenes that are captured in hyperspectral images.
Additionally, lossless data compression generally refers to a class of data compression algorithms where an original data set may be compressed to produce a new data set, which can subsequently be decompressed to extract the original data set (i.e., the decompressed data set corresponds exactly to the original data set). Lossless compression formats are typically categorized according to the type of data the formats were designed to compress (e.g., the ZIP and GZIP file formats provide lossless compression for data, the PNG and GIF provide lossless compression for images, etc.). Although general purpose lossless compression algorithms can theoretically be used on any type of data, existing lossless compression algorithms typically cannot achieve significant compression on a different type data than that which the algorithms were designed to compress. Thus, existing lossless compression algorithms do not provide a suitable guaranteed compression factor for hyperspectral images, and in certain cases, the decompressed data set may even be larger then the original data set.
In the context of hyperspectral images, one technique that has been used to reduce the volume of data relates to reducing the dimensionality of hyperspectral images. In particular, reducing the dimensionality of a data set associated with a hyperspectral image may transform the hyperspectral image into a more compact form, with little to no loss of the most relevant information. Reducing the dimensionality of hyperspectral data is similar in concept to compressing the data, in that the volume of data is reduced, but dimensionality reduction differs from compression in that the compressed data is typically decompressed before being subject to further processing (e.g. to detect targets in a scene, identify changes in successive images of the same scene, etc.). Although dimensionality reduction can be useful in compressing the data set (e.g., to reduce the amount of data transferred to a processing node), the decompressed data typically remains quite large and requires significant computational resources when processed further. Thus, in applications where processing power may be limited or where the data must be processed very rapidly, existing dimensionality reduction techniques do not adequately enable processing of the reduced hyperspectral image data set. In other words, although existing techniques for reducing the dimensionality of hyperspectral images may enable faster transmission of the data set, the existing dimensionality reduction techniques do not alleviate the computational burdens associated with processing the decompressed data set without compromising important information in the data set.
Based on the foregoing, an apparent need exists for a system and method capable of reducing the dimensionality of hyperspectral images in a manner that overcomes one or more of the technical problems noted above.